causal-inference-in-R
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malcolmbarrett
Causal Inference in R: A book!
Once ggdag 0.3.0 is out, I'll need to overhaul the syntax here. Currently, it's a mix of newer and older ways of doing things. Relatedly, in #243, I found that...
Working on the sensitivity chapter has led me to a bit of a deep dive into using some variables from `touringplans::parks_metadata_raw` to better capture variables related to the crowd flow...
@malcolmbarrett requested an issue instead of a PR, so here is a copy of examples from the G-computation chapter I posted here: https://github.com/r-causal/causal-inference-in-R/pull/239 ```r # A linear model for wait_minutes_posted_avg...
Original: 2. **Exchangeability**: We assume that within levels of relevant variables (confounders), exposed and unexposed subjects have an equal likelihood of experiencing any outcome prior to exposure; i.e. the exposed...
Consider reducing the number of times you use "we assume" and "assumption(s)" in 1. Consistency, 2. Exchangeability and 3. Positivity. It's already implied in the list/outline structure.
I've had an idea set seed in my mind for the chapter on evidence about *statistical* inference. We can't cover this topic in depth but we can clarify what we're...
Which appears to improve subjective interpretation of inferential results https://www.pnas.org/doi/full/10.1073/pnas.2302491120 Requires a marginalized approach in some capacity for most problems in the book
I'd like to throw in a callout box about how you can take the weighted outcome model from IPW and do g-computation with it if, say, you want to calculate...
https://stefvanbuuren.name/fimd/sec-nonignorable.html#sec:nonignorable https://stefvanbuuren.name/fimd/sec-sensitivity.html